Clustering algorithms in ad hoc networks

Clustering algorithms in ad hoc networks
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自组织网络中的聚类算法

DOI:
10.1002/ecjb.20143
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发表时间:
2005
期刊:
Electronics and Communications in Japan Part Ii-electronics
影响因子:
--
通讯作者:
H. Fujiwara
H. Fujiwara
中科院分区:
--
文献类型:
--
作者:
Hirohito Taniguchi;M. Inoue;T. Masuzawa;H. Fujiwara

文献摘要

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本研究提出了用于自组织网络(仅包括移动终端的网络)的聚类算法。聚类算法是将整个网络划分为簇的方法,每个簇包括簇头和簇成员,即可以直接与簇头通信的节点。由于移动终端在处理能力和通信能力方面具有相对较低的性能,因此移动终端上的负载应保持尽可能低。分布式系统的问题之一是终端的移动和拓扑的变化会产生开销,必须考虑这一点。此外,集群提供了分层结构,这在无线信道带宽的空间复用方面是有利的。应通过使用更少的簇头并最小化簇头的修改来最小化网络中的信息交换。在本研究中,提出了一种新的聚类算法以及重新聚类算法来处理移动终端移动引起的拓扑变化。仿真表明,除了密集图之外,所提出的聚类算法比传统算法产生更少的聚类,并且所提出的重新聚类算法导致更少的聚类和簇头修改。 © 2004 Wiley periodicals, Inc. Electron Comm Jpn Pt 2, 88(1): 51–59, 2005;在线发表于 Wiley InterScience (www.interscience.wiley.com)。 DOI 10.1002/ecjb.20143
This study proposes clustering algorithms for ad hoc networks (networks including only mobile terminals). A clustering algorithm is a method of dividing the whole network into clusters so that every cluster includes a cluster head and cluster members, that is, nodes that can directly communicate with the cluster head. Since mobile terminals have relatively low performance in terms of processing power and communications capabilities, the load on mobile terminals should be kept as low as possible. One of the problems in distributed systems is that movement of terminals and changes of topology generate overhead, which must be taken into account. In addition, clustering offers a hierarchical structure, which is advantageous in terms of spatial reuse of wireless channel bandwidth. Information exchange in a network should be minimized by using fewer cluster heads, and by minimizing the modifications of cluster heads. In this study, a new clustering algorithm is proposed, along with a reclustering algorithm to deal with topology changes caused by movement of mobile terminals. Simulations show that the proposed clustering algorithm results in fewer clusters than conventional algorithms, except for dense graphs, and that the proposed reclustering algorithm results in fewer clusters and cluster head modifications. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(1): 51–59, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20143